Systems | Development | Analytics | API | Testing

Imagine an airline system monitoring traffic around an airport. If it detects a major delay, countless systems may need to react instantly: Ground operations to adjust flows. Some of these systems will still connect via API, traditional MQ or iPaaS technologies, but the data’s volume and urgency and the ease of decoupling apps make architecting with Kafka the better fit. The natural question is: should all these applications & systems connect to the same Kafka cluster?

We are pleased to announce the launch of the QualityKiosk–Katalon Joint Innovation Lab on October 15th, marking a significant milestone in our shared mission to advance the future of test automation and quality engineering. This strategic collaboration between QualityKiosk Technologies and Katalon reflects our commitment to empowering enterprises with prebuilt, next-generation testing and automation solutions designed to enhance agility, efficiency, and innovation.

The new StudioAssist Agent Mode turns your AI assistant in Katalon Studio into a connected, context-aware testing partner. It now supports MCP Servers, HTTP-based services that let the agent fetch real-time information and perform actions directly inside your project. Katalon ships with two built-in MCP Servers: You can also add your own HTTP-based MCP Servers to extend StudioAssist’s reach. (Note: authentication support is coming soon.)

That’s a wrap on API Summit 2025! At our eighth annual event, the brightest minds in the worlds of APIs and AI gathered in New York City to define the next chapter of digital innovation. We're entering an era where APIs are not just connecting services but connecting intelligence. APIs are the neural pathways of this new AI world, where agents will reason, act, and collaborate through endpoints. At this year's API Summit, we saw how quickly this vision is becoming reality.

As a developer, when you think about CI/CD, you probably focus on build times, test results, and deployment pipelines. The infrastructure powering those builds? It's invisible (unless something goes wrong!). At Bitrise, we've spent 10 years refining infrastructure decisions that most developers never see. In this post, we are pulling back the curtain on the infrastructure choices we've made and why they matter for reliability, consistency, and performance.

APIs and AI are on a collision course. For decades, APIs have been the foundation of digital reliability: deterministic systems where you send a request, get a predictable response, and trust that what’s defined is what will happen. AI doesn’t play by those rules. Large language models and AI agents operate in probabilities. They don’t just follow contracts; they interpret them. They learn, infer, and sometimes hallucinate.

Your CEO just announced the company’s AI-first strategy and the product team is shipping AI features faster than ever. Marketing is promising intelligent automation to customers, while the QA team is left wondering how to actually test this stuff. Every QA team is grappling with the same challenge as AI becomes the default solution for everything from customer service to content generation.

Traditional test metrics like automation %, pass/fail rates, and defect counts don’t reflect the impact of introducing agents into the QA process. This blog explores a new class of KPIs designed to measure how well your virtual test team is performing including Agent Assist Rate, Human Override Rate, Scenario Coverage Delta, and Review Time Saved.

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